{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What's this TensorFlow business?\n",
    "\n",
    "You've written a lot of code in this assignment to provide a whole host of neural network functionality. Dropout, Batch Norm, and 2D convolutions are some of the workhorses of deep learning in computer vision. You've also worked hard to make your code efficient and vectorized.\n",
    "\n",
    "For the last part of this assignment, though, we're going to leave behind your beautiful codebase and instead migrate to one of two popular deep learning frameworks: in this instance, TensorFlow (or PyTorch, if you switch over to that notebook)\n",
    "\n",
    "#### What is it?\n",
    "TensorFlow is a system for executing computational graphs over Tensor objects, with native support for performing backpropogation for its Variables. In it, we work with Tensors which are n-dimensional arrays analogous to the numpy ndarray.\n",
    "\n",
    "#### Why?\n",
    "\n",
    "* Our code will now run on GPUs! Much faster training. Writing your own modules to run on GPUs is beyond the scope of this class, unfortunately.\n",
    "* We want you to be ready to use one of these frameworks for your project so you can experiment more efficiently than if you were writing every feature you want to use by hand. \n",
    "* We want you to stand on the shoulders of giants! TensorFlow and PyTorch are both excellent frameworks that will make your lives a lot easier, and now that you understand their guts, you are free to use them :) \n",
    "* We want you to be exposed to the sort of deep learning code you might run into in academia or industry. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How will I learn TensorFlow?\n",
    "\n",
    "TensorFlow has many excellent tutorials available, including those from [Google themselves](https://www.tensorflow.org/get_started/get_started).\n",
    "\n",
    "Otherwise, this notebook will walk you through much of what you need to do to train models in TensorFlow. See the end of the notebook for some links to helpful tutorials if you want to learn more or need further clarification on topics that aren't fully explained here."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Datasets\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import math\n",
    "import timeit\n",
    "import matplotlib.pyplot as plt\n",
    "import datetime\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train data shape:  (49000, 32, 32, 3)\n",
      "Train labels shape:  (49000,)\n",
      "Validation data shape:  (1000, 32, 32, 3)\n",
      "Validation labels shape:  (1000,)\n",
      "Test data shape:  (10000, 32, 32, 3)\n",
      "Test labels shape:  (10000,)\n"
     ]
    }
   ],
   "source": [
    "from cs231n.data_utils import load_CIFAR10\n",
    "\n",
    "def get_CIFAR10_data(num_training=49000, num_validation=1000, num_test=10000):\n",
    "    \"\"\"\n",
    "    Load the CIFAR-10 dataset from disk and perform preprocessing to prepare\n",
    "    it for the two-layer neural net classifier. These are the same steps as\n",
    "    we used for the SVM, but condensed to a single function.  \n",
    "    \"\"\"\n",
    "    # Load the raw CIFAR-10 data\n",
    "    cifar10_dir = 'cs231n/datasets/cifar-10-batches-py'\n",
    "    X_train, y_train, X_test, y_test = load_CIFAR10(cifar10_dir)\n",
    "\n",
    "    # Subsample the data\n",
    "    mask = range(num_training, num_training + num_validation)\n",
    "    X_val = X_train[mask]\n",
    "    y_val = y_train[mask]\n",
    "    mask = range(num_training)\n",
    "    X_train = X_train[mask]\n",
    "    y_train = y_train[mask]\n",
    "    mask = range(num_test)\n",
    "    X_test = X_test[mask]\n",
    "    y_test = y_test[mask]\n",
    "\n",
    "    # Normalize the data: subtract the mean image\n",
    "    mean_image = np.mean(X_train, axis=0)\n",
    "    X_train -= mean_image\n",
    "    X_val -= mean_image\n",
    "    X_test -= mean_image\n",
    "\n",
    "    return X_train, y_train, X_val, y_val, X_test, y_test\n",
    "\n",
    "\n",
    "# Invoke the above function to get our data.\n",
    "X_train, y_train, X_val, y_val, X_test, y_test = get_CIFAR10_data()\n",
    "print('Train data shape: ', X_train.shape)\n",
    "print('Train labels shape: ', y_train.shape)\n",
    "print('Validation data shape: ', X_val.shape)\n",
    "print('Validation labels shape: ', y_val.shape)\n",
    "print('Test data shape: ', X_test.shape)\n",
    "print('Test labels shape: ', y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example Model\n",
    "\n",
    "### Some useful utilities\n",
    "\n",
    ". Remember that our image data is initially N x H x W x C, where:\n",
    "* N is the number of datapoints\n",
    "* H is the height of each image in pixels\n",
    "* W is the height of each image in pixels\n",
    "* C is the number of channels (usually 3: R, G, B)\n",
    "\n",
    "This is the right way to represent the data when we are doing something like a 2D convolution, which needs spatial understanding of where the pixels are relative to each other. When we input image data into fully connected affine layers, however, we want each data example to be represented by a single vector -- it's no longer useful to segregate the different channels, rows, and columns of the data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The example model itself\n",
    "\n",
    "The first step to training your own model is defining its architecture.\n",
    "\n",
    "Here's an example of a convolutional neural network defined in TensorFlow -- try to understand what each line is doing, remembering that each layer is composed upon the previous layer. We haven't trained anything yet - that'll come next - for now, we want you to understand how everything gets set up. \n",
    "\n",
    "In that example, you see 2D convolutional layers (Conv2d), ReLU activations, and fully-connected layers (Linear). You also see the Hinge loss function, and the Adam optimizer being used. \n",
    "\n",
    "Make sure you understand why the parameters of the Linear layer are 5408 and 10.\n",
    "\n",
    "### TensorFlow Details\n",
    "In TensorFlow, much like in our previous notebooks, we'll first specifically initialize our variables, and then our network model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# clear old variables\n",
    "tf.reset_default_graph()\n",
    "\n",
    "# setup input (e.g. the data that changes every batch)\n",
    "# The first dim is None, and gets sets automatically based on batch size fed in\n",
    "X = tf.placeholder(tf.float32, [None, 32, 32, 3])\n",
    "y = tf.placeholder(tf.int64, [None])\n",
    "is_training = tf.placeholder(tf.bool)\n",
    "global_step = tf.Variable(0)\n",
    "\n",
    "Wconv1 = tf.get_variable(\"Wconv1\", shape=[7, 7, 3, 32])\n",
    "bconv1 = tf.get_variable(\"bconv1\", shape=[32])\n",
    "W1 = tf.get_variable(\"W1\", shape=[5408, 10])\n",
    "b1 = tf.get_variable(\"b1\", shape=[10])\n",
    "\n",
    "def simple_model(X,y):\n",
    "    # define our weights (e.g. init_two_layer_convnet)\n",
    "    # setup variables\n",
    "#     Wconv1 = tf.get_variable(\"Wconv1\", shape=[7, 7, 3, 32])\n",
    "#     bconv1 = tf.get_variable(\"bconv1\", shape=[32])\n",
    "#     W1 = tf.get_variable(\"W1\", shape=[5408, 10])\n",
    "#     b1 = tf.get_variable(\"b1\", shape=[10])\n",
    "    # define our graph (e.g. two_layer_convnet)\n",
    "    a1 = tf.nn.conv2d(X, Wconv1, strides=[1,2,2,1], padding='VALID') + bconv1\n",
    "    h1 = tf.nn.relu(a1)\n",
    "    h1_flat = tf.reshape(h1,[-1,5408])\n",
    "    y_out = tf.matmul(h1_flat,W1) + b1\n",
    "    return y_out\n",
    "\n",
    "y_out = simple_model(X,y)\n",
    "\n",
    "# define our loss\n",
    "total_loss = tf.losses.hinge_loss(tf.one_hot(y,10),logits=y_out)\n",
    "mean_loss = tf.reduce_mean(total_loss)\n",
    "mean_loss += 0.001 * (tf.nn.l2_loss(Wconv1) + tf.nn.l2_loss(W1))\n",
    "\n",
    "# define our optimizer\n",
    "learning_rate = tf.train.exponential_decay(0.001, global_step, 100, 0.9, staircase=True)\n",
    "optimizer = tf.train.AdamOptimizer(learning_rate) # select optimizer and set learning rate\n",
    "train_step = optimizer.minimize(mean_loss, global_step=global_step)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "TensorFlow supports many other layer types, loss functions, and optimizers - you will experiment with these next. Here's the official API documentation for these (if any of the parameters used above were unclear, this resource will also be helpful). \n",
    "\n",
    "* Layers, Activations, Loss functions : https://www.tensorflow.org/api_guides/python/nn\n",
    "* Optimizers: https://www.tensorflow.org/api_guides/python/train#Optimizers\n",
    "* BatchNorm: https://www.tensorflow.org/api_docs/python/tf/layers/batch_normalization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training the model on one epoch\n",
    "While we have defined a graph of operations above, in order to execute TensorFlow Graphs, by feeding them input data and computing the results, we first need to create a `tf.Session` object. A session encapsulates the control and state of the TensorFlow runtime. For more information, see the TensorFlow [Getting started](https://www.tensorflow.org/get_started/get_started) guide.\n",
    "\n",
    "Optionally we can also specify a device context such as `/cpu:0` or `/gpu:0`. For documentation on this behavior see [this TensorFlow guide](https://www.tensorflow.org/tutorials/using_gpu)\n",
    "\n",
    "You should see a validation loss of around 0.4 to 0.6 and an accuracy of 0.30 to 0.35 below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training\n",
      "Iteration 0: with minibatch training loss = 7.93 and accuracy of 0.094\n",
      "Iteration 100: with minibatch training loss = 1.12 and accuracy of 0.2\n",
      "Iteration 200: with minibatch training loss = 0.704 and accuracy of 0.39\n",
      "Iteration 300: with minibatch training loss = 0.683 and accuracy of 0.3\n",
      "Iteration 400: with minibatch training loss = 0.585 and accuracy of 0.3\n",
      "Iteration 500: with minibatch training loss = 0.567 and accuracy of 0.42\n",
      "Iteration 600: with minibatch training loss = 0.477 and accuracy of 0.27\n",
      "Iteration 700: with minibatch training loss = 0.534 and accuracy of 0.36\n",
      "Epoch 1, Overall loss = 0.762 and accuracy of 0.303\n"
     ]
    },
    {
     "data": {
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Gufvzief7A78EjgH6A7cUj3mzk8/VaR0REZFu6OnTOn2r/YZd5e7HpDhmAmEWT0fPLwVO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\nJcopqfVExdMbNnLUc9IVK1e6/+tfoZc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/fiiYosG9EF97qvwL/ZxzSq+tdNddYZwQuH/z\nm2Ff8tIT0ZdkNLW/oy/lvfYqfVx+uYjPfa79aRwIP7fo8yttAwbEa9MsWhTWptl5546PL99++MOO\nn9t337gHKzkLLdpmzQoF6O9+V7o/Kvh+/OPwe4j2J9fyeeKJeNzSD38Yfoara2uy1+aFF7r0x69T\nKk56aFNxIiJdtXRp+J9oR6KiobwIWBMPPxwKh3KXXRaPReiuhQvbL05XbsWKkCfZC/XUUyHnT38a\nxoJ0JNmrdfXV4TVbbNH+mOSX6QknhNNjr70Wemmi/b/6VTj+zjvDabif/SyMJ2lpCQXO3XeH42fP\njt/7ttvCa5ODcKPehXLf+lbpMZdeGoqwT30qZIzW0Fl33TBWqnxRxp//PIwjeeed8LtZd93wZ2XR\notB7NnZsOM2VnJKd7IF77rlQjEengaOid8WKMOMqGlsVnSqLBso+8EB43X33lQ48P+WUeJByNOU+\nOSV+Tak4UXEiIr1Io/Ty9LT77+/aGIa33gqL5pVPvXcPYzzuuiv0DCQLmmgW1iGHdL+dM2eWFkDf\n/W48GDVp+fLwJR99kVcqus45p3TRxOXL4xlQaa8RVS4a8NvZuLQZM+IesWiNn1tvrXzsqFFhILN7\nyHn44aFnCKp7aQYVJypO1lgellJ2z09O9/xkVc5s6U7OZ58NY4vW1M47h1Mkq/PCC/GaP2mVr0fS\nlZwLF5YOyF6d6DRbZ1d5LxcNMv7739O/ZnV6ujjpi2Te6NGj692EmshLTshPVuXMlu7k/PCHq/PZ\nM2emO2677WD8+K6999prlz7uSs711w9bWscdB8OGwQ47pH/NeuuF20WL0r+m3sxDL0LumNkQoLm5\nuZkhQ4bUuzkiIiI9wh369oVLL4Xjj6/Oe7a0tLDHHnsA7OHuLdV519ha1X5DERERaRxmoXdm8eJ6\ntyQ9ndYRERHJuDPOgKFD692K9NRzkgNTp06tdxNqIi85IT9ZlTNblLN+xoyBvfeudyvSy1xxYmaj\nzOxFM3vPzB4ws4/Xu031NnHixHo3oSbykhPyk1U5s0U5Ja1MFSdmdjTwK2A8sDvwGHCrmW1S14bV\n2aabblrvJtREXnJCfrIqZ7Yop6SVqeIEGANc7u5/cPdZwPFAKzCyvs0SERGRtDJTnJjZ+4A9gNuj\nfR7mSf8LGFavdomIiEjXZKY4ATYB+gBzy/bPBQbVvjkiIiLSHXmeSrw2wNNPP13vdvS4hx56iJaW\nqq+R03DykhPyk1U5s0U5syPx3bl2Z8d1V2ZWiC2e1mkFvuTuNyb2/w7YwN2/UHb8V4H/q2kjRURE\nsuVr7v7nar9pZnpO3H25mTUDnwVuBDAzKz6+uMJLbgW+BrwELKlRM0VERLJgbWAbwndp1WWm5wTA\nzL4M/I4wS+chwuydI4GPuPu8OjZNREREUspMzwmAu19TXNPkLGBz4FHgQBUmIiIivUemek5ERESk\n98vSVGIRERHJABUnIiIi0lByW5z09gsEmtk+Znajmb1mZm1mVqhwzFlmNsfMWs1smpltX/Z8fzO7\n1Mzmm9liM7vWzDarXYrOmdlpZvaQmS0ys7lmdr2Z7VDhuN6e83gze8zMFha3+8zsoLJjenXGSszs\nR8U/u+eX7e/1Wc1sfDFbcnuq7Jhen5P/3965B2tVlWH89xCiHokYFUhGwRsijYEW3i+YkpmJjGVC\neEf/yMbRyhkc1MK7ZN4hq9GkUMyyzLLQqQazAtRM0xQRApWKS4gkjCAiZ/XHWkf32X58h0PnnO87\n6zy/mT2cddv7fdb62Pvd67IXIKm/pHuSnevSb/kTpTydWmt6VpTbs1HSlEKeTq0RQFI3SVdLWpx0\n/EPS5RXytb/WEEKXO4AxxOXDZwL7At8H3gB2rrVtrdBwPHHi72hgE3BSKf2SpOlEYD/gIWAR0KOQ\n57vEpdQjiBslzgH+VGttBftmAmcAQ4CPA79O9m6fmc7PpfbcC9gbuAbYAAzJRWMFzQcCi4FngZtz\nas9k4yTgeaAP0DcdO2aoszfwCnAXcfuQgcBIYI+ctAI7FdqxL/ETFZuAI3PRmGy8FPhPuh8NAD4P\nrAEu6Oj2rHll1KgBngBuK4QF/AuYUGvbtlJPIx90TpYCXyuEewHrgVML4Q3AyYU8g9O5Dqq1ps3o\n3DnZd0TOOpONq4BzctQI9AReBo4BHqO5c5KFVqJz8kyV9Fx0TgYebyFPFlpLmm4FFuSmEXgYuLMU\n9zNgekdr7XLDOuoCGwRK2oO4n1BR4xrgSd7XOJy4lLyY52VgCfVbD72BQPTas9SZulXHAg3AnBw1\nAt8BHg4hzCpGZqh1kOKw6yJJ90raDbLTOQp4WtJP09DrM5LOa0rMTCvw3jPkNOAHKZyTxjnAsZIG\nAUgaBhxO7MXuUK1ZfedkC6m2QeDgjjenXfgo8SFebRPEfsA76Ye1uTx1gyQR31b+HEJoGrvPRqek\n/YC5xK8uriW+dbws6VAy0QiQHK/9iTewMtm0J7F39mxiD9EuwBXAH1M756RzT+B84CbgWuAg4HZJ\nG0II95CX1iZOBj4C/CiFc9I4mdjzMV/SJuK81MtCCPen9A7T2hWdE9M5uQP4GNGLz5H5wDDiTe8U\nYLqko2prUtsiaVeigzkyhLCx1va0JyGE4ie9X5D0FPAacCqxrXOhG/BUCOEbKfxccsC+DNxTO7Pa\nlfHAIyGE5bU2pB0YA4wDxgLziC8St0lampzNDqPLDesArxMnMvUrxfcDcvmxLSfOo6mmcTnQQ1Kv\nKnnqAklTgROAo0MIywpJ2egMIbwbQlgcQng2hHAZ8BxwERlpJA6n9gGekbRR0kbihLmLJL1DfLPK\nRWszQghvAguIE55zatNlQHlr95eIkykhL61IGkCc8HtnITonjTcAk0MID4QQXgwhzABuASam9A7T\n2uWck/TG1rRBINBsg8A5tbKrLQkhvEL8ERQ19gIO5n2NfwXeLeUZTLypzO0wY1sgOSajgU+FEJYU\n03LSWYFuwLaZafw9cdXV/sReomHA08C9wLAQwmLy0doMST2JjsnSzNp0Nh8cDh9M7CXK8f/oeKIT\nPbMpIjONDcSX9yKNJF+hQ7XWenZwLQ5i1+o6mi8lXgX0qbVtrdCwA/Hmvn/68Xw1hXdL6ROSplHE\nB8JDwEKaL/e6g7gM8GjiW+1s6mhpW7JvNXAk0etuOrYr5MlB53VJ40Di0rzr03/uY3LRWEV7ebVO\nFlqBbwNHpTY9DPgd8aG2U2Y6hxNXZkwkLoUfR5wzNTbDNhVxeey1FdJy0TiNOHH1hPTbPZm4tPi6\njtZa88qoYSN8Jf3Q1hO9ueG1tqmV9o8gOiWbSsfdhTxXEJd9rSNua7136RzbAlOIQ11rgQeAvrXW\nVrCvkr5NwJmlfJ1d513Eb36sJ76V/JbkmOSisYr2WRSck1y0Aj8mfp5gfbrZ30fh2x+56Ex2nkD8\npss64EVgfIU8nV4r8Ol0/9l7M+k5aNwBuJnoWLxFdDquBLp3tFZv/GeMMcaYuqLLzTkxxhhjTH1j\n58QYY4wxdYWdE2OMMcbUFXZOjDHGGFNX2DkxxhhjTF1h58QYY4wxdYWdE2OMMcbUFXZOjDHGGFNX\n2DkxJkMkjZC0qcLmW9XKTJP0YCH8mKSb28fCqnYMlNQoaWhHX3trSfaeVGs7jMmF7rU2wBjTLswG\ndgkhrGlFmQuJ+4e0GZJGEPfP6d1KW/zpamO6MHZOjMmQEMK7xA27WlNmbTuYIqKj0Vqnp02dpM6I\npG1C3EXdmC6Hh3WMqXPS8Mrtkm6R9Iak5ZLOldQg6W5JayQtlHR8ocyINNTQK4XPkrRa0nGS5kla\nK+kRSf0KZZoN6yS6S5oi6b+SVkq6qmTb6ZL+kmxYJmmGpD4pbSBxYz+A1WmY6e6UJkkTkt1vS3pV\n0sTStfeSNEvSW5L+JumQFuqpMdXLg6nMAkmjCulnSVpdKjNaUmMhPEnSs5LOkfRaqqepkrole5dJ\nWiHp0gom9Jc0U9I6SYskfaF0rV0l/SS1wypJD6U6Ktb/LyRdKunfwPxqeo3JGTsnxnQOzgRWAgcC\ntwPfI+70ORs4gLiT8XRJ2xXKlIdGGoCLgdOAI4EBwI0tXPdsYGO67oXA1yWdW0jvDlwODAVGE7dZ\nn5bS/gk0PaAHAbsAF6XwZOLW61cCQ4AxxB2Zi1wD3AAMAxYA90lq6Z71TeB+4lbuM4EZknoX0isN\nF5Xj9gKOBz4DjAXOA34D9AeOAi4BrpF0YKncVcQ2GQrMAO6XNBhAUnfi7q1vAocDhxF3a300pTVx\nLLAPMBI4sQWtxuRLrbdo9uHDR/WDOGfj8UK4G/HB9sNCXD+gETgohUcQt3fvlcJnpfDuhTLnA0sL\n4WnAg6XrvlCy5fpyXCl9eLpOQyU7UlxPYD1wzmbOMTBpObsQNySdZ58q124EriiEG1LccYU6eKNU\nZjSwqRCelOq2oRD3CLCoVO4lYELp2lNLeeY2xQGnA/NK6T2I29KPLNT/Ukrb0/vw0RUP95wY0zl4\nvumPEEIjsAr4eyFuRfqzb5VzrAshvFoIL2shP8ATpfBcYJAkAUj6pKRfpSGQNcAfUr4BVc45hPhg\nnlUlDxT0JVu1BfYW62QdsGYLypR5NZVtYgUwr5RnRYXzVqqrIenvocR6W9t0ENtwW2JPzXv2hzhf\nyJgujSfEGtM5KE+MDBXioPpQbaVzbPXEU0kNwKPEnoVxxGGngSmuR5Wi67fwEkV7m4ZeWnqhqqSx\nqUwjH9S7zRaeo9p5t4SewNPEeirbsLLw91utOKcx2eKeE2NMNQ4uhQ8FFoYQArAvsCMwMYQwO4Sw\ngDi8VOSd9O+HCnELgbeJ8ys2R3ssJV4JfFjS9oW4A9rw/OUJu4cQh38AniHOu1kZQlhcOtpjlZQx\nnRo7J8bkS1ssxx0g6UZJ+0j6EnABcGtKW0J0Pi6UtEf6CNnlpfKvER2NUZJ2lrRDCGED8C3gBkln\nSNpT0sGSxrex7WWeBNYB16drjiPOQ2krvphW+QySdCVxEvHUlDYDeB34paQjJO0u6WhJt0nq34Y2\nGJMFdk6MqX+2ZIVJpbj/t/chANOB7YGngCnALSGEuwBCCK8TV/OcArxIXH1zcbMThLCUOMl0MnE1\nzpSUdDVwE3G1zjziCps+Ldjekp6qZUIIq4kTUz9LnMMzJtm2NVSq60nE1T3PpeuMDSHMT9deT1zp\nswT4OVHzncQ5J635OJ0xXQLF3lljjDHGmPrAPSfGGGOMqSvsnBhjjDGmrrBzYowxxpi6ws6JMcYY\nY+oKOyfGGGOMqSvsnBhjjDGmrrBzYowxxpi6ws6JMcYYY+oKOyfGGGOMqSvsnBhjjDGmrrBzYowx\nxpi6ws6JMcYYY+qK/wF9MZnbHBgTRgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xd994794c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation\n",
      "Epoch 1, Overall loss = 0.399 and accuracy of 0.37\n",
      "Test\n",
      "Epoch 1, Overall loss = 0.396 and accuracy of 0.366\n"
     ]
    }
   ],
   "source": [
    "def run_model(session, predict, loss_val, Xd, yd,\n",
    "              epochs=1, batch_size=64, print_every=100,\n",
    "              training=None, plot_losses=False):\n",
    "    # have tensorflow compute accuracy\n",
    "    correct_prediction = tf.equal(tf.argmax(predict,1), y)\n",
    "    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n",
    "    \n",
    "    # shuffle indicies\n",
    "    train_indicies = np.arange(Xd.shape[0])\n",
    "    np.random.shuffle(train_indicies)\n",
    "\n",
    "    training_now = training is not None\n",
    "    \n",
    "    # setting up variables we want to compute (and optimizing)\n",
    "    # if we have a training function, add that to things we compute\n",
    "    variables = [mean_loss,correct_prediction,accuracy]\n",
    "    if training_now:\n",
    "        variables[-1] = training\n",
    "    \n",
    "    # counter \n",
    "    iter_cnt = 0\n",
    "    for e in range(epochs):\n",
    "        # keep track of losses and accuracy\n",
    "        correct = 0\n",
    "        losses = []\n",
    "        # make sure we iterate over the dataset once\n",
    "        for i in range(int(math.ceil(Xd.shape[0]/batch_size))):\n",
    "            # generate indicies for the batch\n",
    "            start_idx = (i*batch_size)%Xd.shape[0]\n",
    "            idx = train_indicies[start_idx:start_idx+batch_size]\n",
    "            \n",
    "            # create a feed dictionary for this batch\n",
    "            feed_dict = {X: Xd[idx,:],\n",
    "                         y: yd[idx],\n",
    "                         is_training: training_now }\n",
    "            # get batch size\n",
    "            actual_batch_size = yd[idx].shape[0]\n",
    "            \n",
    "            # have tensorflow compute loss and correct predictions\n",
    "            # and (if given) perform a training step\n",
    "            loss, corr, _ = session.run(variables,feed_dict=feed_dict)\n",
    "            \n",
    "            # aggregate performance stats\n",
    "            losses.append(loss*actual_batch_size)\n",
    "            correct += np.sum(corr)\n",
    "            \n",
    "            # print every now and then\n",
    "            if training_now and (iter_cnt % print_every) == 0:\n",
    "                print(\"Iteration {0}: with minibatch training loss = {1:.3g} and accuracy of {2:.2g}\"\\\n",
    "                      .format(iter_cnt,loss,np.sum(corr)/actual_batch_size))\n",
    "            iter_cnt += 1\n",
    "        total_correct = correct/Xd.shape[0]\n",
    "        total_loss = np.sum(losses)/Xd.shape[0]\n",
    "        print(\"Epoch {2}, Overall loss = {0:.3g} and accuracy of {1:.3g}\"\\\n",
    "              .format(total_loss,total_correct,e+1))\n",
    "        if plot_losses:\n",
    "            plt.plot(losses)\n",
    "            plt.grid(True)\n",
    "            plt.title('Epoch {} Loss'.format(e+1))\n",
    "            plt.xlabel('minibatch number')\n",
    "            plt.ylabel('minibatch loss')\n",
    "            plt.show()\n",
    "    return total_loss,total_correct\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    with tf.device(\"/cpu:0\"): #\"/cpu:0\" or \"/gpu:0\"\n",
    "        sess.run(tf.global_variables_initializer())\n",
    "        print('Training')\n",
    "        run_model(sess,y_out,mean_loss,X_train,y_train,1,64,100,train_step,True)\n",
    "        print('Validation')\n",
    "        run_model(sess,y_out,mean_loss,X_val,y_val,1,64)\n",
    "        print('Test')\n",
    "        run_model(sess,y_out,mean_loss,X_test,y_test,1,64)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training a specific model\n",
    "\n",
    "In this section, we're going to specify a model for you to construct. The goal here isn't to get good performance (that'll be next), but instead to get comfortable with understanding the TensorFlow documentation and configuring your own model. \n",
    "\n",
    "Using the code provided above as guidance, and using the following TensorFlow documentation, specify a model with the following architecture:\n",
    "\n",
    "* 7x7 Convolutional Layer with 32 filters and stride of 1\n",
    "* ReLU Activation Layer\n",
    "* Spatial Batch Normalization Layer (trainable parameters, with scale and centering)\n",
    "* 2x2 Max Pooling layer with a stride of 2\n",
    "* Affine layer with 1024 output units\n",
    "* ReLU Activation Layer\n",
    "* Affine layer from 1024 input units to 10 outputs\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# clear old variables\n",
    "tf.reset_default_graph()\n",
    "\n",
    "# define our input (e.g. the data that changes every batch)\n",
    "# The first dim is None, and gets sets automatically based on batch size fed in\n",
    "X = tf.placeholder(tf.float32, [None, 32, 32, 3])\n",
    "y = tf.placeholder(tf.int64, [None])\n",
    "is_training = tf.placeholder(tf.bool)\n",
    "global_step = tf.Variable(0)\n",
    "    # setup variables\n",
    "Wconv1 = tf.get_variable(\"Wconv1\", shape=[7, 7, 3, 32])\n",
    "bconv1 = tf.get_variable(\"bconv1\", shape=[32])\n",
    "W1 = tf.get_variable(\"W1\", shape=[8192, 1024])\n",
    "b1 = tf.get_variable(\"b1\", shape=[1024])\n",
    "W2 = tf.get_variable(\"W2\", shape=[1024, 10])\n",
    "b2 = tf.get_variable(\"b2\", shape=[10])\n",
    "# define model\n",
    "def complex_model(X,y,is_training):\n",
    "#     # setup variables\n",
    "#     Wconv1 = tf.get_variable(\"Wconv1\", shape=[7, 7, 3, 32])\n",
    "#     bconv1 = tf.get_variable(\"bconv1\", shape=[32])\n",
    "#     W1 = tf.get_variable(\"W1\", shape=[8192, 1024])\n",
    "#     b1 = tf.get_variable(\"b1\", shape=[1024])\n",
    "#     W2 = tf.get_variable(\"W2\", shape=[1024, 10])\n",
    "#     b2 = tf.get_variable(\"b2\", shape=[10])\n",
    "    # define our graph (e.g. two_layer_convnet)\n",
    "    a1 = tf.nn.conv2d(X, Wconv1, strides=[1,1,1,1], padding='SAME') + bconv1\n",
    "    h_batch = tf.layers.batch_normalization(a1, center=True, scale=True,  training=is_training)\n",
    "    h1 = tf.nn.relu(h_batch)\n",
    "    h_pool = tf.nn.max_pool(h1, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')\n",
    "    conv_flat = tf.reshape(h_pool,[-1,8192])\n",
    "    h_affine = tf.matmul(conv_flat,W1) + b1\n",
    "    h_batch2 = tf.layers.batch_normalization(h_affine, center=True, scale=True,  training=is_training)\n",
    "    h_relu = tf.nn.relu(h_batch2)\n",
    "    y_out = tf.matmul(h_relu,W2) + b2\n",
    "    return y_out\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To make sure you're doing the right thing, use the following tool to check the dimensionality of your output (it should be 64 x 10, since our batches have size 64 and the output of the final affine layer should be 10, corresponding to our 10 classes):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10 loops, best of 3: 127 ms per loop\n",
      "(64, 10)\n",
      "True\n"
     ]
    }
   ],
   "source": [
    "# Now we're going to feed a random batch into the model \n",
    "# and make sure the output is the right size\n",
    "x = np.random.randn(64, 32, 32,3)\n",
    "with tf.Session() as sess:\n",
    "    with tf.device(\"/cpu:0\"): #\"/cpu:0\" or \"/gpu:0\"\n",
    "        tf.global_variables_initializer().run()\n",
    "\n",
    "        ans = sess.run(y_out,feed_dict={X:x,is_training:True})\n",
    "        %timeit sess.run(y_out,feed_dict={X:x,is_training:True})\n",
    "        print(ans.shape)\n",
    "        print(np.array_equal(ans.shape, np.array([64, 10])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You should see the following from the run above \n",
    "\n",
    "`(64, 10)`\n",
    "\n",
    "`True`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### GPU!\n",
    "\n",
    "Now, we're going to try and start the model under the GPU device, the rest of the code stays unchanged and all our variables and operations will be computed using accelerated code paths. However, if there is no GPU, we get a Python exception and have to rebuild our graph. On a dual-core CPU, you might see around 50-80ms/batch running the above, while the Google Cloud GPUs (run below) should be around 2-5ms/batch."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10 loops, best of 3: 117 ms per loop\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    with tf.Session() as sess:\n",
    "        with tf.device(\"/cpu:0\") as dev: #\"/cpu:0\" or \"/gpu:0\"\n",
    "            tf.global_variables_initializer().run()\n",
    "\n",
    "            ans = sess.run(y_out,feed_dict={X:x,is_training:True})\n",
    "            %timeit sess.run(y_out,feed_dict={X:x,is_training:True})\n",
    "except tf.errors.InvalidArgumentError:\n",
    "    print(\"no gpu found, please use Google Cloud if you want GPU acceleration\")    \n",
    "    # rebuild the graph\n",
    "    # trying to start a GPU throws an exception \n",
    "    # and also trashes the original graph\n",
    "    tf.reset_default_graph()\n",
    "    X = tf.placeholder(tf.float32, [None, 32, 32, 3])\n",
    "    y = tf.placeholder(tf.int64, [None])\n",
    "    is_training = tf.placeholder(tf.bool)\n",
    "    y_out = complex_model(X,y,is_training)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You should observe that even a simple forward pass like this is significantly faster on the GPU. So for the rest of the assignment (and when you go train your models in assignment 3 and your project!), you should use GPU devices. However, with TensorFlow, the default device is a GPU if one is available, and a CPU otherwise, so we can skip the device specification from now on."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train the model.\n",
    "\n",
    "Now that you've seen how to define a model and do a single forward pass of some data through it, let's  walk through how you'd actually train one whole epoch over your training data (using the complex_model you created provided above).\n",
    "\n",
    "Make sure you understand how each TensorFlow function used below corresponds to what you implemented in your custom neural network implementation.\n",
    "\n",
    "First, set up an **RMSprop optimizer** (using a 1e-3 learning rate) and a **cross-entropy loss** function. See the TensorFlow documentation for more information\n",
    "* Layers, Activations, Loss functions : https://www.tensorflow.org/api_guides/python/nn\n",
    "* Optimizers: https://www.tensorflow.org/api_guides/python/train#Optimizers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training\n",
      "Iteration 0: with minibatch training loss = 3.53 and accuracy of 0.094\n",
      "Iteration 100: with minibatch training loss = 2.62 and accuracy of 0.48\n",
      "Iteration 200: with minibatch training loss = 2.33 and accuracy of 0.5\n",
      "Iteration 300: with minibatch training loss = 2.27 and accuracy of 0.41\n",
      "Iteration 400: with minibatch training loss = 1.67 and accuracy of 0.64\n",
      "Iteration 500: with minibatch training loss = 1.62 and accuracy of 0.56\n",
      "Iteration 600: with minibatch training loss = 1.5 and accuracy of 0.59\n",
      "Iteration 700: with minibatch training loss = 1.73 and accuracy of 0.42\n",
      "Epoch 1, Overall loss = 2.11 and accuracy of 0.465\n"
     ]
    },
    {
     "data": {
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OdKF2KknRnJMycfzxXNl14UL2iLjU1XHI57vfDe8fODAI79iIOJk/n4UNEbDd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\niIiIlIqKExERESmV/w8Ax0oBSmJ2TgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xd99657f9e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation\n",
      "Epoch 1, Overall loss = 1.64 and accuracy of 0.522\n",
      "Test\n",
      "Epoch 1, Overall loss = 1.64 and accuracy of 0.538\n"
     ]
    }
   ],
   "source": [
    "y_out = complex_model(X,y,is_training)\n",
    "    # Inputs\n",
    "    #     y_out: is what your model computes\n",
    "    #     y: is your TensorFlow variable with label information\n",
    "    # Outputs\n",
    "    #    mean_loss: a TensorFlow variable (scalar) with numerical loss\n",
    "    #    optimizer: a TensorFlow optimizer\n",
    "    # This should be ~3 lines of code!\n",
    "mean_loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=tf.one_hot(y,10), logits=y_out))\n",
    "mean_loss += 0.001 * (tf.nn.l2_loss(Wconv1) + tf.nn.l2_loss(W1) + tf.nn.l2_loss(W2))\n",
    "\n",
    "learning_rate = tf.train.exponential_decay(0.001, global_step, 100, 0.9, staircase=True)\n",
    "optimizer = tf.train.RMSPropOptimizer(learning_rate)#.minimize(mean_loss, global_step=global_step)\n",
    "\n",
    "extra_update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)\n",
    "with tf.control_dependencies(extra_update_ops):\n",
    "    train_step = optimizer.minimize(mean_loss, global_step=global_step)\n",
    "    \n",
    "with tf.Session() as sess:\n",
    "    with tf.device(\"/cpu:0\"): #\"/cpu:0\" or \"/gpu:0\" \n",
    "        sess.run(tf.global_variables_initializer())\n",
    "        print('Training')\n",
    "        run_model(sess,y_out,mean_loss,X_train,y_train,1,64,100,train_step,True)\n",
    "        print('Validation')\n",
    "        run_model(sess,y_out,mean_loss,X_val,y_val,1,64)\n",
    "        print('Test')\n",
    "        run_model(sess,y_out,mean_loss,X_test,y_test,1,64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# batch normalization in tensorflow requires this extra dependency\n",
    "# extra_update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)\n",
    "# with tf.control_dependencies(extra_update_ops):\n",
    "#     train_step = optimizer.minimize(mean_loss)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train the model\n",
    "Below we'll create a session and train the model over one epoch. You should see a loss of 1.4 to 2.0 and an accuracy of 0.4 to 0.5. There will be some variation due to random seeds and differences in initialization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training\n",
      "Iteration 0: with minibatch training loss = 38.4 and accuracy of 0.094\n",
      "Iteration 100: with minibatch training loss = 20.9 and accuracy of 0.11\n",
      "Iteration 200: with minibatch training loss = 2.94 and accuracy of 0.16\n",
      "Iteration 300: with minibatch training loss = 2.81 and accuracy of 0.16\n",
      "Iteration 400: with minibatch training loss = 1.9 and accuracy of 0.33\n",
      "Iteration 500: with minibatch training loss = 1.84 and accuracy of 0.38\n",
      "Iteration 600: with minibatch training loss = 1.66 and accuracy of 0.38\n",
      "Iteration 700: with minibatch training loss = 1.26 and accuracy of 0.58\n",
      "Epoch 1, Overall loss = 4.39 and accuracy of 0.281\n",
      "Iteration 800: with minibatch training loss = 1.34 and accuracy of 0.48\n",
      "Iteration 900: with minibatch training loss = 1.4 and accuracy of 0.55\n",
      "Iteration 1000: with minibatch training loss = 1.3 and accuracy of 0.56\n",
      "Iteration 1100: with minibatch training loss = 1.66 and accuracy of 0.33\n",
      "Iteration 1200: with minibatch training loss = 1.17 and accuracy of 0.56\n",
      "Iteration 1300: with minibatch training loss = 1.51 and accuracy of 0.41\n",
      "Iteration 1400: with minibatch training loss = 1.13 and accuracy of 0.58\n",
      "Iteration 1500: with minibatch training loss = 1.08 and accuracy of 0.64\n",
      "Epoch 2, Overall loss = 1.37 and accuracy of 0.517\n",
      "Iteration 1600: with minibatch training loss = 1.28 and accuracy of 0.59\n",
      "Iteration 1700: with minibatch training loss = 1.3 and accuracy of 0.55\n",
      "Iteration 1800: with minibatch training loss = 1.01 and accuracy of 0.64\n",
      "Iteration 1900: with minibatch training loss = 1.01 and accuracy of 0.64\n",
      "Iteration 2000: with minibatch training loss = 0.989 and accuracy of 0.67\n",
      "Iteration 2100: with minibatch training loss = 0.944 and accuracy of 0.69\n",
      "Iteration 2200: with minibatch training loss = 1.16 and accuracy of 0.62\n",
      "Epoch 3, Overall loss = 1.12 and accuracy of 0.614\n",
      "Iteration 2300: with minibatch training loss = 0.906 and accuracy of 0.7\n",
      "Iteration 2400: with minibatch training loss = 1.04 and accuracy of 0.66\n",
      "Iteration 2500: with minibatch training loss = 0.792 and accuracy of 0.67\n",
      "Iteration 2600: with minibatch training loss = 0.941 and accuracy of 0.67\n",
      "Iteration 2700: with minibatch training loss = 1 and accuracy of 0.67\n",
      "Iteration 2800: with minibatch training loss = 0.857 and accuracy of 0.75\n",
      "Iteration 2900: with minibatch training loss = 0.942 and accuracy of 0.61\n",
      "Iteration 3000: with minibatch training loss = 0.982 and accuracy of 0.66\n",
      "Epoch 4, Overall loss = 0.981 and accuracy of 0.663\n",
      "Iteration 3100: with minibatch training loss = 0.975 and accuracy of 0.69\n",
      "Iteration 3200: with minibatch training loss = 1.2 and accuracy of 0.58\n",
      "Iteration 3300: with minibatch training loss = 0.771 and accuracy of 0.75\n",
      "Iteration 3400: with minibatch training loss = 0.636 and accuracy of 0.78\n",
      "Iteration 3500: with minibatch training loss = 0.883 and accuracy of 0.75\n",
      "Iteration 3600: with minibatch training loss = 0.742 and accuracy of 0.73\n",
      "Iteration 3700: with minibatch training loss = 0.676 and accuracy of 0.83\n",
      "Iteration 3800: with minibatch training loss = 0.891 and accuracy of 0.7\n",
      "Epoch 5, Overall loss = 0.917 and accuracy of 0.688\n",
      "Iteration 3900: with minibatch training loss = 0.802 and accuracy of 0.77\n",
      "Iteration 4000: with minibatch training loss = 1.15 and accuracy of 0.56\n",
      "Iteration 4100: with minibatch training loss = 0.791 and accuracy of 0.67\n",
      "Iteration 4200: with minibatch training loss = 0.941 and accuracy of 0.67\n",
      "Iteration 4300: with minibatch training loss = 1.07 and accuracy of 0.62\n",
      "Iteration 4400: with minibatch training loss = 1.03 and accuracy of 0.66\n",
      "Iteration 4500: with minibatch training loss = 0.956 and accuracy of 0.67\n",
      "Epoch 6, Overall loss = 0.887 and accuracy of 0.7\n",
      "Iteration 4600: with minibatch training loss = 1.11 and accuracy of 0.61\n",
      "Iteration 4700: with minibatch training loss = 0.856 and accuracy of 0.64\n",
      "Iteration 4800: with minibatch training loss = 0.803 and accuracy of 0.77\n",
      "Iteration 4900: with minibatch training loss = 0.812 and accuracy of 0.69\n",
      "Iteration 5000: with minibatch training loss = 1.04 and accuracy of 0.7\n",
      "Iteration 5100: with minibatch training loss = 0.978 and accuracy of 0.66\n",
      "Iteration 5200: with minibatch training loss = 0.951 and accuracy of 0.69\n",
      "Iteration 5300: with minibatch training loss = 0.779 and accuracy of 0.66\n",
      "Epoch 7, Overall loss = 0.873 and accuracy of 0.705\n",
      "Iteration 5400: with minibatch training loss = 1.03 and accuracy of 0.69\n",
      "Iteration 5500: with minibatch training loss = 1.21 and accuracy of 0.56\n",
      "Iteration 5600: with minibatch training loss = 0.786 and accuracy of 0.72\n",
      "Iteration 5700: with minibatch training loss = 0.902 and accuracy of 0.69\n",
      "Iteration 5800: with minibatch training loss = 0.729 and accuracy of 0.77\n",
      "Iteration 5900: with minibatch training loss = 0.795 and accuracy of 0.73\n",
      "Iteration 6000: with minibatch training loss = 0.938 and accuracy of 0.69\n",
      "Iteration 6100: with minibatch training loss = 0.861 and accuracy of 0.69\n",
      "Epoch 8, Overall loss = 0.866 and accuracy of 0.708\n",
      "Iteration 6200: with minibatch training loss = 1.04 and accuracy of 0.56\n",
      "Iteration 6300: with minibatch training loss = 0.871 and accuracy of 0.72\n",
      "Iteration 6400: with minibatch training loss = 1 and accuracy of 0.67\n",
      "Iteration 6500: with minibatch training loss = 0.954 and accuracy of 0.66\n",
      "Iteration 6600: with minibatch training loss = 0.877 and accuracy of 0.75\n",
      "Iteration 6700: with minibatch training loss = 1.09 and accuracy of 0.69\n",
      "Iteration 6800: with minibatch training loss = 0.726 and accuracy of 0.72\n",
      "Epoch 9, Overall loss = 0.862 and accuracy of 0.71\n",
      "Iteration 6900: with minibatch training loss = 0.935 and accuracy of 0.67\n",
      "Iteration 7000: with minibatch training loss = 0.73 and accuracy of 0.73\n",
      "Iteration 7100: with minibatch training loss = 0.773 and accuracy of 0.77\n",
      "Iteration 7200: with minibatch training loss = 0.721 and accuracy of 0.73\n",
      "Iteration 7300: with minibatch training loss = 1.06 and accuracy of 0.62\n",
      "Iteration 7400: with minibatch training loss = 1.13 and accuracy of 0.67\n",
      "Iteration 7500: with minibatch training loss = 0.956 and accuracy of 0.7\n",
      "Iteration 7600: with minibatch training loss = 0.858 and accuracy of 0.75\n",
      "Epoch 10, Overall loss = 0.86 and accuracy of 0.71\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0.86043171475858105, 0.71014285714285719)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sess = tf.Session()\n",
    "\n",
    "sess.run(tf.global_variables_initializer())\n",
    "print('Training')\n",
    "run_model(sess,y_out,mean_loss,X_train,y_train,10,64,100,optimizer)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check the accuracy of the model.\n",
    "\n",
    "Let's see the train and test code in action -- feel free to use these methods when evaluating the models you develop below. You should see a loss of 1.3 to 2.0 with an accuracy of 0.45 to 0.55."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation\n",
      "Epoch 1, Overall loss = 1.02 and accuracy of 0.672\n",
      "Test\n",
      "Epoch 1, Overall loss = 1 and accuracy of 0.665\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(1.0003420480728149, 0.66520000000000001)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Validation')\n",
    "run_model(sess,y_out,mean_loss,X_val,y_val,1,64)\n",
    "print('Test')\n",
    "run_model(sess,y_out,mean_loss,X_test,y_test,1,64)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train a _great_ model on CIFAR-10!\n",
    "\n",
    "Now it's your job to experiment with architectures, hyperparameters, loss functions, and optimizers to train a model that achieves ** >= 70% accuracy on the validation set** of CIFAR-10. You can use the `run_model` function from above."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Things you should try:\n",
    "- **Filter size**: Above we used 7x7; this makes pretty pictures but smaller filters may be more efficient\n",
    "- **Number of filters**: Above we used 32 filters. Do more or fewer do better?\n",
    "- **Pooling vs Strided Convolution**: Do you use max pooling or just stride convolutions?\n",
    "- **Batch normalization**: Try adding spatial batch normalization after convolution layers and vanilla batch normalization after affine layers. Do your networks train faster?\n",
    "- **Network architecture**: The network above has two layers of trainable parameters. Can you do better with a deep network? Good architectures to try include:\n",
    "    - [conv-relu-pool]xN -> [affine]xM -> [softmax or SVM]\n",
    "    - [conv-relu-conv-relu-pool]xN -> [affine]xM -> [softmax or SVM]\n",
    "    - [batchnorm-relu-conv]xN -> [affine]xM -> [softmax or SVM]\n",
    "- **Use TensorFlow Scope**: Use TensorFlow scope and/or [tf.layers](https://www.tensorflow.org/api_docs/python/tf/layers) to make it easier to write deeper networks. See [this tutorial](https://www.tensorflow.org/tutorials/layers) for how to use `tf.layers`. \n",
    "- **Use Learning Rate Decay**: [As the notes point out](http://cs231n.github.io/neural-networks-3/#anneal), decaying the learning rate might help the model converge. Feel free to decay every epoch, when loss doesn't change over an entire epoch, or any other heuristic you find appropriate. See the [Tensorflow documentation](https://www.tensorflow.org/versions/master/api_guides/python/train#Decaying_the_learning_rate) for learning rate decay.\n",
    "- **Global Average Pooling**: Instead of flattening and then having multiple affine layers, perform convolutions until your image gets small (7x7 or so) and then perform an average pooling operation to get to a 1x1 image picture (1, 1 , Filter#), which is then reshaped into a (Filter#) vector. This is used in [Google's Inception Network](https://arxiv.org/abs/1512.00567) (See Table 1 for their architecture).\n",
    "- **Regularization**: Add l2 weight regularization, or perhaps use [Dropout as in the TensorFlow MNIST tutorial](https://www.tensorflow.org/get_started/mnist/pros)\n",
    "\n",
    "### Tips for training\n",
    "For each network architecture that you try, you should tune the learning rate and regularization strength. When doing this there are a couple important things to keep in mind:\n",
    "\n",
    "- If the parameters are working well, you should see improvement within a few hundred iterations\n",
    "- Remember the coarse-to-fine approach for hyperparameter tuning: start by testing a large range of hyperparameters for just a few training iterations to find the combinations of parameters that are working at all.\n",
    "- Once you have found some sets of parameters that seem to work, search more finely around these parameters. You may need to train for more epochs.\n",
    "- You should use the validation set for hyperparameter search, and we'll save the test set for evaluating your architecture on the best parameters as selected by the validation set.\n",
    "\n",
    "### Going above and beyond\n",
    "If you are feeling adventurous there are many other features you can implement to try and improve your performance. You are **not required** to implement any of these; however they would be good things to try for extra credit.\n",
    "\n",
    "- Alternative update steps: For the assignment we implemented SGD+momentum, RMSprop, and Adam; you could try alternatives like AdaGrad or AdaDelta.\n",
    "- Alternative activation functions such as leaky ReLU, parametric ReLU, ELU, or MaxOut.\n",
    "- Model ensembles\n",
    "- Data augmentation\n",
    "- New Architectures\n",
    "  - [ResNets](https://arxiv.org/abs/1512.03385) where the input from the previous layer is added to the output.\n",
    "  - [DenseNets](https://arxiv.org/abs/1608.06993) where inputs into previous layers are concatenated together.\n",
    "  - [This blog has an in-depth overview](https://chatbotslife.com/resnets-highwaynets-and-densenets-oh-my-9bb15918ee32)\n",
    "\n",
    "If you do decide to implement something extra, clearly describe it in the \"Extra Credit Description\" cell below.\n",
    "\n",
    "### What we expect\n",
    "At the very least, you should be able to train a ConvNet that gets at **>= 70% accuracy on the validation set**. This is just a lower bound - if you are careful it should be possible to get accuracies much higher than that! Extra credit points will be awarded for particularly high-scoring models or unique approaches.\n",
    "\n",
    "You should use the space below to experiment and train your network. The final cell in this notebook should contain the training and validation set accuracies for your final trained network.\n",
    "\n",
    "Have fun and happy training!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Feel free to play with this cell\n",
    "\n",
    "def my_model(X,y,is_training):\n",
    "    # con bn pool con bn pool affine\n",
    "    conv1 = tf.nn.conv2d(X, Wconv1, strides=[1,1,1,1], padding='SAME') + bconv1\n",
    "    h_batch1 = tf.layers.batch_normalization(conv1, center=True, scale=True,  training=is_training)\n",
    "    h1 = tf.nn.relu(h_batch1)\n",
    "    h_pool1 = tf.nn.max_pool(h1, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')\n",
    "    \n",
    "    conv2 = tf.nn.conv2d(h_pool1, Wconv2, strides=[1,1,1,1], padding='SAME') + bconv2\n",
    "    h_batch2 = tf.layers.batch_normalization(conv2, center=True, scale=True,  training=is_training)\n",
    "    h2 = tf.nn.relu(h_batch2)\n",
    "    h_pool2 = tf.nn.max_pool(h2, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')\n",
    "    \n",
    "    conv_flat = tf.reshape(h_pool2,[-1,4096])\n",
    "    affine = tf.matmul(conv_flat,W1) + b1\n",
    "    h_batch3 = tf.layers.batch_normalization(affine, center=True, scale=True,  training=is_training)\n",
    "    h3 = tf.nn.relu(h_batch3)\n",
    "    y_out = tf.matmul(h3,W2) + b2\n",
    "    return y_out\n",
    "\n",
    "tf.reset_default_graph()\n",
    "\n",
    "X = tf.placeholder(tf.float32, [None, 32, 32, 3])\n",
    "y = tf.placeholder(tf.int64, [None])\n",
    "is_training = tf.placeholder(tf.bool)\n",
    "global_step = tf.Variable(0)\n",
    "\n",
    "Wconv1 = tf.get_variable(\"Wconv1\", shape=[5, 5, 3, 32])\n",
    "bconv1 = tf.get_variable(\"bconv1\", shape=[32])\n",
    "Wconv2 = tf.get_variable(\"Wconv2\", shape=[5, 5, 32, 64])\n",
    "bconv2 = tf.get_variable(\"bconv2\", shape=[64])\n",
    "\n",
    "W1 = tf.get_variable(\"W1\", shape=[4096, 1024])\n",
    "b1 = tf.get_variable(\"b1\", shape=[1024])\n",
    "W2 = tf.get_variable(\"W2\", shape=[1024, 10])\n",
    "b2 = tf.get_variable(\"b2\", shape=[10])\n",
    "    \n",
    "    \n",
    "y_out = my_model(X,y,is_training)\n",
    "mean_loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=tf.one_hot(y,10), logits=y_out))\n",
    "mean_loss += 0.001 * (tf.nn.l2_loss(Wconv1) + tf.nn.l2_loss(Wconv2) + tf.nn.l2_loss(W1))\n",
    "\n",
    "learning_rate = tf.train.exponential_decay(0.001, global_step, 100, 0.9, staircase=True)\n",
    "optimizer = tf.train.AdamOptimizer(learning_rate)\n",
    "\n",
    "# batch normalization in tensorflow requires this extra dependency\n",
    "extra_update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)\n",
    "with tf.control_dependencies(extra_update_ops):\n",
    "    train_step = optimizer.minimize(mean_loss, global_step=global_step)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training\n",
      "Iteration 0: with minibatch training loss = 3.65 and accuracy of 0.16\n",
      "Iteration 100: with minibatch training loss = 2.44 and accuracy of 0.42\n",
      "Iteration 200: with minibatch training loss = 2.02 and accuracy of 0.56\n",
      "Iteration 300: with minibatch training loss = 1.83 and accuracy of 0.56\n",
      "Iteration 400: with minibatch training loss = 1.59 and accuracy of 0.59\n",
      "Iteration 500: with minibatch training loss = 1.45 and accuracy of 0.61\n",
      "Iteration 600: with minibatch training loss = 1.53 and accuracy of 0.56\n",
      "Iteration 700: with minibatch training loss = 1.64 and accuracy of 0.58\n",
      "Epoch 1, Overall loss = 1.85 and accuracy of 0.562\n"
     ]
    },
    {
     "data": {
      "image/png": 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LTbGrCEazJXYxwY8BRGQnbJ7LI14blmLL6A/N7Nofa7T8c2YBc7xz8rL11vDQ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\nAsam1s1Fg139cj9gCXBosLwMOCTYZ0R0rh2LXGc04KZNm+b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4+cY34s/FijimqaS8QLz+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      "text/plain": [
       "<matplotlib.figure.Figure at 0xd9992228d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 800: with minibatch training loss = 1.31 and accuracy of 0.66\n",
      "Iteration 900: with minibatch training loss = 1.26 and accuracy of 0.7\n",
      "Iteration 1000: with minibatch training loss = 1.05 and accuracy of 0.72\n",
      "Iteration 1100: with minibatch training loss = 1 and accuracy of 0.72\n",
      "Iteration 1200: with minibatch training loss = 1.14 and accuracy of 0.72\n",
      "Iteration 1300: with minibatch training loss = 1.02 and accuracy of 0.7\n",
      "Iteration 1400: with minibatch training loss = 0.908 and accuracy of 0.75\n",
      "Iteration 1500: with minibatch training loss = 1.12 and accuracy of 0.72\n",
      "Epoch 2, Overall loss = 1.13 and accuracy of 0.711\n"
     ]
    },
    {
     "data": {
      "image/png": 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MmUC/fsA3vhGu3CDCdnClfr7Fdek3jUClUurnxlJdlM3Pj4j+RkT7EtF2RLQb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\noiiK0iPc3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      "text/plain": [
       "<matplotlib.figure.Figure at 0xd99a7eec88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 1600: with minibatch training loss = 1.09 and accuracy of 0.73\n",
      "Iteration 1700: with minibatch training loss = 0.851 and accuracy of 0.78\n",
      "Iteration 1800: with minibatch training loss = 0.974 and accuracy of 0.73\n",
      "Iteration 1900: with minibatch training loss = 0.801 and accuracy of 0.8\n",
      "Iteration 2000: with minibatch training loss = 0.674 and accuracy of 0.89\n",
      "Iteration 2100: with minibatch training loss = 0.729 and accuracy of 0.86\n",
      "Iteration 2200: with minibatch training loss = 0.718 and accuracy of 0.83\n",
      "Epoch 3, Overall loss = 0.801 and accuracy of 0.81\n"
     ]
    },
    {
     "data": {
      "image/png": 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9A4hQyxnM/JDhmG6uFkdivvgC2HZboH9/4KmnKl0ah8PhqAylnqulGnq1/BPApUT0BYD3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4VHjTN9+vRKFyEz6sVWtjNfsJ35ol7sLCcsPGqc9vb2ShchM+rFVrYzX7Cd+aJe7Cwn3KuFYRiG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\nwTAMwzBMZvx/EgTRz64LeboAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xd99a695cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 2300: with minibatch training loss = 0.812 and accuracy of 0.8\n",
      "Iteration 2400: with minibatch training loss = 0.652 and accuracy of 0.88\n",
      "Iteration 2500: with minibatch training loss = 0.523 and accuracy of 0.94\n",
      "Iteration 2600: with minibatch training loss = 0.536 and accuracy of 0.91\n",
      "Iteration 2700: with minibatch training loss = 0.725 and accuracy of 0.86\n",
      "Iteration 2800: with minibatch training loss = 0.673 and accuracy of 0.83\n",
      "Iteration 2900: with minibatch training loss = 0.495 and accuracy of 0.94\n",
      "Iteration 3000: with minibatch training loss = 0.585 and accuracy of 0.89\n",
      "Epoch 4, Overall loss = 0.577 and accuracy of 0.894\n"
     ]
    },
    {
     "data": {
      "image/png": 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114F33/XbNu/yNCmqW3Sj7dS54w6gf//a3nV5UKSdjaRV7MyTNMJjbwCPVn+f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\nzyeNx+NnABYC2BvAVgA+oH1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mAQ88kGGmcmCs2ZZuoRm7ZGdJmu60Q4noNgDvQk4M97jx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\nDMMwDMMUjf8Pzh7cdIHdWR0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xd99a449860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 3100: with minibatch training loss = 0.533 and accuracy of 0.91\n",
      "Iteration 3200: with minibatch training loss = 0.503 and accuracy of 0.95\n",
      "Iteration 3300: with minibatch training loss = 0.398 and accuracy of 0.97\n",
      "Iteration 3400: with minibatch training loss = 0.504 and accuracy of 0.91\n",
      "Iteration 3500: with minibatch training loss = 0.374 and accuracy of 0.98\n",
      "Iteration 3600: with minibatch training loss = 0.34 and accuracy of 1\n",
      "Iteration 3700: with minibatch training loss = 0.493 and accuracy of 0.92\n",
      "Iteration 3800: with minibatch training loss = 0.471 and accuracy of 0.89\n",
      "Epoch 5, Overall loss = 0.454 and accuracy of 0.941\n"
     ]
    },
    {
     "data": {
      "image/png": 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qHxYeDMMwDMOUDRYeNc6ECRMqnYWyUS5bK9291ihlynbWF2wn4wsLjxpn/Pjxlc5C2WgU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xH5XbeVekOPcrWU9xgTUmVdYIsHw6SQMWNqXYNsUe7O3DXfBiMZPBg49dTgf5SCEBVc6nJxqOXF\nKh5JXTSVyOOhl2sb1VIuVLlpsi7Uo8WDYzwYhklEPb1ZNRqlvOmPHVveulQbPWdHpRWPNN0D9Wjx\nYMWjzlm8eHGtq1A1GkVWljNbVFPOYhWPlSuBm24q7dhJ5azUg3LcuEq7Whan6iFfjwH0rHjUOe3t\n7bWuQtVoFFlZzvSTJBCzmnL6ZC61seeehaNdkuIrp1m3c84BRo1KdqxBg+T31luHl7/yCnDttZUb\nmi0VmvZUKR716GrhGI8654477qh1FapGo8jaaHKmqRP34b77gL328t++mu1ZqTd9H5LKqdr9xz9O\nfqwpU6TSMXFieHlUzo9yIBWaO1J1zdajq4UVD4ZhEtHoQaDHHFPrGrjxyVxaa8px/RABn/98dY6l\nk8YYj3q0eKT48mQYJo1MmSJHCn3mM7WuCWNSS4tHUir58K5U2WlUPNjiwTBM5hkyROZGYdJHGh+M\nJtVUiioTXJqu88sWD6bqTJ06tdZVqBqNImujyZmmTrwSVLM9VXCpK1lYJWmE61YqdlNTdc2mURmK\no+aKBxHNJKKnieg9IlpHRPcQ0R6W7S4hoteIqJOIHiai3WpR37TRKFkugcaRleXMFtWUU1k8aqF4\npKk9d91VugRnzy5vuWnMXFqPFo80uFrGApgH4M+Q9bkCwBIi2lsIsQEAiOgCyAT5JwNYA+AyAL/O\nb/NxTWqdEiZPnlzrKlSNRpGV5cwW1ZRz3jw5/4uafA4A3nyzOsf2lXObbeT34YdXri4tLcDCheUv\nVz7kJ2PcuPKXXSz1aPGoueIhhDha/09EpwB4A8D+AJbmF38XwKVCiF/ltzkZwDoAXwRwZ9UqyzAM\nk2KGDweuuy68bODA2tTFxTbbAO++C/TrV+uaFMfrrwfKUxrgBGLlYWsAAsBbAEBEOwMYCuA3agMh\nxHsAngLAs3AwTJ1TT29qTHno378+Rt7YGDKkcPbbWlKPrpZUKR5ERACuBbBUCLEiv3gopCKyzth8\nXX5dQ7N06dL4jTJCo8jaaHKOGFHjilSYRmvPrJM2OevR1ZIqxQPADQA+CeDEchR29NFHI5fLhT5j\nxowpmFNgyZIlyOVyBfufccYZWLBgQWhZR0cHcrkc1q9fH1o+a9YszJkzJ7Rs7dq1yOVyWLlyZWj5\nvHnzcP7554eWdXZ2IpfLFVzU7e3t1mjxSZMmYfHixZg7d24m5NBxyfG1r30tE3LEtYfepvUsh45N\njtmzZyOXy2HOnJVYvrx+5YhrD70961kOHZscc+fOrTs5gE4Aydrj7LPPTpUc0uLRieuuK+66am9v\n7342Dh06FLlcrkDGckMiJWoSEc0H8AUAY4UQa7XlOwN4CcB+QohnteW/B7BcCFFwhohoFIBly5Yt\nw6ikkwDUGZ2dnehT7HzWdUajyMpyZguWM70oN0WSx2Da5BRCWj1uugn4xjfKU2ZHRwf2339/ANhf\nCNFRnlIDUmHxyCsdxwL4D13pAAAhxMsAXgcwXtu+P4DRABo+jVGaboBK0yiyspzZguXMFmmTsx5j\nPGoeIkNENwCYDCAH4EMiGpJf9a4Q4qP872sB/ICIXoQcTnspgFcB3Fvl6jIMwzAZ4Q9/AAYMqHUt\nSqepqb5iPGqueAD4NmTw6O+N5VMB3A4AQoi5RNQHwI2Qo14eA/C5Rs/hwTAMwxTP2LG1rkF5IKov\ni0fNXS1CiCYhRLPlc7ux3WwhxDAhRB8hxGeFEC/Wqs5pojBYKrs0iqwsZ7ZgObNFGuWsN4tHzRUP\npjSGDx9e6ypUjUaRleXMFixntkijnE1N9WXxSM2olnLSSKNaGIZhmMamd29gzhzgzDPLU15DjGph\nGIZhGKY42NXCMAzDMEzV4OBSpqqYWe2yTKPIynJmC5YzW6RRTrZ4MFVlxowZta5C1WgUWVnObMFy\nZos0yskWD6aqzJ8/v9ZVqBqNIivLmS1YzmyRRjnZ4sFUlTQO7aoUjSIry5ktWM5skUY52eLBMAzD\nMEzVYIsHwzAMwzBVo94SiLHiUefMmTOn1lWoGo0iK8uZLVjObJFGOdnVwlSVzs7OWlehajSKrCxn\ntmA5s0Ua5aw3VwunTGcYhmGYOmbYMOBb3wJmzSpPeZwynWEYhmEYJ/Vm8WDFg2EYhmHqGI7xYKrK\n+vXra12FqtEosrKc2YLlzBZplJMtHkxVmTZtWq2rUDUaRVaWM1uwnNkijXKyxYOpKrNnz651FapG\no8jKcmYLljNbpFFOtngwVaWRRu00iqwsZ7ZgObNFGuXkBGIMwzAMw1QNdrUwDMMwDFM12NXCVJUF\nCxbUugpVo1FkZTmzBcuZLdIoJ1s8mKrS0VH2pHKppVFkZTmzBcuZLdIoZ71ZPDhlOsMwDMPUMZ/6\nFPCZzwDXXlue8jhlOsMwDMMwTurN4sGKB8MwDMPUMRzjwTAMwzBM1WCLB1NVcrlcratQNRpFVpYz\nW7Cc2SKNcnICMaaqTJ8+vdZVqBqNIivLmS1YzmyRRjnrzdXCo1oYhmEYpo458EBg1CjgxhvLU16l\nR7W0lLtAhmEYhmGqx+TJwHbb1boW/rDiwTAMwzB1zDnn1LoGyeAYjzpn8eLFta5C1WgUWVnObMFy\nZotGkbOSpELxIKKxRPRLIvonEXURUc5Yf2t+uf55oFb1TRNz5sypdRWqRqPIynJmC5YzWzSKnJUk\nFYoHgDYAfwFwOgBXtOuDAIYAGJr/TK5O1dLNoEGDal2FqtEosrKc2YLlzBaNImclSUWMhxDiIQAP\nAQARkWOzjUKIN6tXK4ZhGIZhyk1aLB4+jCOidUS0kohuIKIBta4QwzAMwzDJSIXFw4MHAfwcwMsA\ndgVwBYAHiGiMyGIiEoZhGIbJKHWheAgh7tT+Pk9EfwXwEoBxAH5n2aUVAP72t79VvnI15umnn0ZH\nR9nzu6SSRpGV5cwWLGe2aAQ5tWdnayXKT13mUiLqAvBFIcQvY7Z7A8D3hRA3WdZNAbCwQlVkGIZh\nmEbgJCHE/5W70LqweJgQ0fYAtgHwL8cmvwZwEoA1AD6qUrUYhmEYJgu0AtgJ8lladlJh8SCiNgC7\nASAAHQDOgXShvJX/zIKM8Xg9v90cyCG4I4QQm2pRZ4ZhGIZhkpMWxeMISEXDrMxtkLk9FgPYD8DW\nAF6D1ML+i4fXMgzDMEx9kQrFg2EYhmGYxqCe8ngwDMMwDFPnsOLBMAzDMEzVyKTiQURnENHLRLSB\niJ4kogNrXackxE2al9/mEiJ6jYg6iehhItrNWN+LiK4novVE9D4R3U1Eg6snRTRENJOIniai9/IZ\nae8hoj0s29W7nN8momeI6N3853EimmhsU9cy2iCi7+Wv3auN5XUvKxHNskxaucLYpu7lBAAiGkZE\nP8vXszN/LY8ytqlrWfPPCrM9u4honrZNXcsIAETURESXEtHqvBwvEtEPLNtVXlYhRKY+ACZBDqE9\nGcBeAG6EHBkzsNZ1SyDDRACXADgWwBYAOWP9BXmZjgGwD2Tw7UsAemrb/A/kcOIjAHwawOMAHqu1\nbFr9HgDwNQB7A9gXwK/y9e2dMTk/n2/PXSFHZF0GYCOAvbMio0XmAwGsBrAcwNVZas98HWcBeBbA\nIACD858BGZRza8hs0TcD2B/AjgCOArBzlmSFTM0wWPuMh+x3x2ZFxnwdLwTwRr4/Gg7gOADvAZhe\n7fas+cmowMl9EsB12n8C8CqAGbWuW5HydKFQ8XgNwNna//4ANgD4ivZ/I4AvadvsmS/roFrL5JBz\nYL5+h2VZznwd/w1gahZlBNAXwN8BHAk5Uk1XPDIhK6Ti0RGxPityXgng0ZhtMiGrIdO1AFZlTUYA\n9wG4yVh2N4Dbqy1rplwtRNQDUjP/jVom5Jl5BMCYWtWrnBDRzgCGIizjewCeQiDjAZDJ4fRt/g5g\nLdJ7HraGHE79FpBNOfOmzhMB9AHweBZlBHA9gPuEEL/VF2ZQ1t1JukJfIqL/R0Q7AJmT8wsA/kxE\nd+bdoR1E9A21MmOyAuh+hpwEYEH+f5ZkfBzAeCLaHQCIaCSAQyGtz1WVtS4zl0YwEEAzgHXG8nWQ\nWlkWGAr5gLbJODT/ewiAj/MXjWub1EBEBPmWsVQIoXzlmZGTiPYB8ARkNsD3Id8W/k5EY5ARGQEg\nr1TtB9k5mWSmPSGtqqdAWna2BTAbwB/y7ZwlOXcB8B0APwbwQwAHAfhvItoohPgZsiWr4ksAtoLM\nIQVkS8YrIS0WK4loC2SM5/eFEIvy66sma9YUD6Y+uQHAJyG17yyyEsBIyA7tBAC3E9Hhta1SeSE5\njcG1AI4SGc8mLITQ00g/R0RPA3gFwFcg2zorNAF4WghxUf7/M3nl6tsAfla7alWUaQAeFEK8XuuK\nVIBJAKYAOBHACsiXhOuI6LW8Ilk1MuVqAbAeMihoiLF8CGS69SzwOmTcSpSMrwPoSUT9I7ZJBUQ0\nH8DRAMYJIfS5dzIjpxBisxBitRBiuRDi+wCeAfBdZEhGSBfnIAAdRLSJiDZBBp99l4g+hnwjyoqs\nIYQQ7wJYBRk8nKU2/RcAc4rvv0EGJgLZkhVENBwyeFafeDRLMs4FcKUQ4i4hxPNCiIUArgEwM7++\narJmSvHIv2ktg4xKBtBtxh8P6d+qe4QQL0M2sC5jfwCjEci4DMBmY5s9ITuMJ6pW2RjySsexAP5D\nCLFWX5clOS00AeiVMRkfgRydtB+kdWckgD8D+H8ARgohViM7soYgor6QSsdrGWvTP6LQRb0npHUn\ni/foNEgF+QG1IGMy9oF8MdfpQl4PqKqstY60rUDk7lcAdCI8nPbfAAbVum4JZGiD7Lj3y18YZ+X/\n75BfPyMv0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      "text/plain": [
       "<matplotlib.figure.Figure at 0xd9950ad0b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 3900: with minibatch training loss = 0.332 and accuracy of 1\n",
      "Iteration 4000: with minibatch training loss = 0.544 and accuracy of 0.89\n",
      "Iteration 4100: with minibatch training loss = 0.37 and accuracy of 0.97\n",
      "Iteration 4200: with minibatch training loss = 0.484 and accuracy of 0.94\n",
      "Iteration 4300: with minibatch training loss = 0.337 and accuracy of 0.97\n",
      "Iteration 4400: with minibatch training loss = 0.34 and accuracy of 0.98\n",
      "Iteration 4500: with minibatch training loss = 0.321 and accuracy of 1\n",
      "Epoch 6, Overall loss = 0.397 and accuracy of 0.96\n"
     ]
    },
    {
     "data": {
      "image/png": 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3JR46QqcjHrZ76fKxTfRpNJE2+PpY2c6b+uY11wgtntT8JYXv3jPF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DomUYQQmfoA2AigFLLPDgA+BXC0z++jAYhly5aJonPaaac1WoTUaBZdWc9iwXoWiyzqudtuQnzn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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xd99a5eb0b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 4600: with minibatch training loss = 0.377 and accuracy of 0.98\n",
      "Iteration 4700: with minibatch training loss = 0.304 and accuracy of 0.98\n",
      "Iteration 4800: with minibatch training loss = 0.458 and accuracy of 0.89\n",
      "Iteration 4900: with minibatch training loss = 0.373 and accuracy of 0.97\n",
      "Iteration 5000: with minibatch training loss = 0.346 and accuracy of 0.97\n",
      "Iteration 5100: with minibatch training loss = 0.345 and accuracy of 0.98\n",
      "Iteration 5200: with minibatch training loss = 0.405 and accuracy of 0.98\n",
      "Iteration 5300: with minibatch training loss = 0.347 and accuracy of 0.97\n",
      "Epoch 7, Overall loss = 0.371 and accuracy of 0.968\n"
     ]
    },
    {
     "data": {
      "image/png": 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Ih6vSFa14dDm29Wwk4kH7qwD5iYfLVheKjPGoheLR29tVdn+OohWP/v50Wbmu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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xd99a6be240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 5400: with minibatch training loss = 0.341 and accuracy of 0.98\n",
      "Iteration 5500: with minibatch training loss = 0.37 and accuracy of 0.98\n",
      "Iteration 5600: with minibatch training loss = 0.313 and accuracy of 1\n",
      "Iteration 5700: with minibatch training loss = 0.319 and accuracy of 0.98\n",
      "Iteration 5800: with minibatch training loss = 0.336 and accuracy of 0.98\n",
      "Iteration 5900: with minibatch training loss = 0.338 and accuracy of 0.97\n",
      "Iteration 6000: with minibatch training loss = 0.424 and accuracy of 0.97\n",
      "Iteration 6100: with minibatch training loss = 0.347 and accuracy of 0.97\n",
      "Epoch 8, Overall loss = 0.359 and accuracy of 0.972\n"
     ]
    },
    {
     "data": {
      "image/png": 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41OXBlwHNYGrJK2uIc6kJfGKvZVTOHXa4NJW/Dltd5X1jljItn6teXHlkqU/V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eWiiDq7bRwP7dWiKIqiKF3MuHGw557wjW9kczzt1aIoiqIoSiTdluMxtNMGKIqiKIrSPB//OLzj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\nN9TxUBRFURQlN9TxUBRFURQlN/4/81AAUQo4thwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xd99a764f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 6200: with minibatch training loss = 0.373 and accuracy of 0.97\n",
      "Iteration 6300: with minibatch training loss = 0.411 and accuracy of 0.95\n",
      "Iteration 6400: with minibatch training loss = 0.41 and accuracy of 0.94\n",
      "Iteration 6500: with minibatch training loss = 0.268 and accuracy of 0.98\n",
      "Iteration 6600: with minibatch training loss = 0.279 and accuracy of 1\n",
      "Iteration 6700: with minibatch training loss = 0.279 and accuracy of 1\n",
      "Iteration 6800: with minibatch training loss = 0.343 and accuracy of 0.98\n",
      "Epoch 9, Overall loss = 0.353 and accuracy of 0.974\n"
     ]
    },
    {
     "data": {
      "image/png": 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ZfUwtJtRK4zFzZkcsTx36BFlpADH9epeM+RKPqD6raWqpduTSpHZ7+ukqbksR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lV1lgvVWS66RWcr0V4tiqIoiqKsQXu1KIqiKIpSGtTxUBRFURQlN9Tx6HBWrFjRbhNyo1u0qs5y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\noiiKoii5oY6HoiiKoii58f8B+aNzhY0YDwwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xd99a809dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 6900: with minibatch training loss = 0.268 and accuracy of 1\n",
      "Iteration 7000: with minibatch training loss = 0.311 and accuracy of 0.98\n",
      "Iteration 7100: with minibatch training loss = 0.341 and accuracy of 0.97\n",
      "Iteration 7200: with minibatch training loss = 0.344 and accuracy of 0.97\n",
      "Iteration 7300: with minibatch training loss = 0.257 and accuracy of 1\n",
      "Iteration 7400: with minibatch training loss = 0.332 and accuracy of 0.98\n",
      "Iteration 7500: with minibatch training loss = 0.354 and accuracy of 1\n",
      "Iteration 7600: with minibatch training loss = 0.386 and accuracy of 0.98\n",
      "Epoch 10, Overall loss = 0.351 and accuracy of 0.975\n"
     ]
    },
    {
     "data": {
      "image/png": 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TS7XJmReonPVMPBqlPvNEIB41jjvuuKPSRbDi8cdF+OQskLesco+RSi+jrvY6zQrVJmde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\ny4Dvftd8b79+5uXwukip7e12U8vzzwP77x+/x6U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e9Lc6BzXKctrfAVgPwG4A1gA4CGKJ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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xd99a8c8c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation\n",
      "Epoch 1, Overall loss = 0.89 and accuracy of 0.765\n",
      "Test\n",
      "Epoch 1, Overall loss = 0.9 and accuracy of 0.762\n"
     ]
    }
   ],
   "source": [
    "# Feel free to play with this cell\n",
    "# This default code creates a session\n",
    "# and trains your model for 10 epochs\n",
    "# then prints the validation set accuracy\n",
    "with tf.Session() as sess:\n",
    "    with tf.device(\"/cpu:0\"): #\"/cpu:0\" or \"/gpu:0\" \n",
    "        sess.run(tf.global_variables_initializer())\n",
    "        print('Training')\n",
    "        run_model(sess,y_out,mean_loss,X_train,y_train,10,64,100,train_step,True)\n",
    "        print('Validation')\n",
    "        run_model(sess,y_out,mean_loss,X_val,y_val,1,64)\n",
    "        print('Test')\n",
    "        run_model(sess,y_out,mean_loss,X_test,y_test,1,64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Test your model here, and make sure \n",
    "# the output of this cell is the accuracy\n",
    "# of your best model on the training and val sets\n",
    "# We're looking for >= 70% accuracy on Validation\n",
    "# print('Training')\n",
    "# run_model(sess,y_out,mean_loss,X_train,y_train,1,128)\n",
    "# print('Validation')\n",
    "# run_model(sess,y_out,mean_loss,X_val,y_val,1,128)\n",
    "# print('Test')\n",
    "# run_model(sess,y_out,mean_loss,X_test,y_test,1,128)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Describe what you did here\n",
    "In this cell you should also write an explanation of what you did, any additional features that you implemented, and any visualizations or graphs that you make in the process of training and evaluating your network"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_Tell us here_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test Set - Do this only once\n",
    "Now that we've gotten a result that we're happy with, we test our final model on the test set. This would be the score we would achieve on a competition. Think about how this compares to your validation set accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
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   "source": [
    "# tf.reset_default_graph()\n",
    "\n",
    "# X = tf.placeholder(tf.float32, [None, 32, 32, 3])\n",
    "# y = tf.placeholder(tf.int64, [None])\n",
    "# is_training = tf.placeholder(tf.bool)\n",
    "# global_step = tf.Variable(0)\n",
    "\n",
    "# def my_model2(X,y,is_training):\n",
    "#     droprate = 1.0\n",
    "#     if is_training is not None:\n",
    "#         droprate = 0.5\n",
    "        \n",
    "#     Wconv11 = tf.get_variable(\"Wconv11\", shape=[3, 3, 3, 30])\n",
    "#     bconv11 = tf.get_variable(\"bconv11\", shape=[30])\n",
    "#     Wconv12 = tf.get_variable(\"Wconv12\", shape=[3, 3, 30, 30])\n",
    "#     bconv12 = tf.get_variable(\"bconv12\", shape=[30])\n",
    "#     Wconv13 = tf.get_variable(\"Wconv13\", shape=[3, 3, 30, 30])\n",
    "#     bconv13 = tf.get_variable(\"bconv13\", shape=[30])\n",
    "\n",
    "#     Wconv21 = tf.get_variable(\"Wconv21\", shape=[3, 3, 30, 50])\n",
    "#     bconv21 = tf.get_variable(\"bconv21\", shape=[50])\n",
    "#     Wconv22 = tf.get_variable(\"Wconv22\", shape=[3, 3, 50, 50])\n",
    "#     bconv22 = tf.get_variable(\"bconv22\", shape=[50])\n",
    "#     Wconv23 = tf.get_variable(\"Wconv23\", shape=[3, 3, 50, 50])\n",
    "#     bconv23 = tf.get_variable(\"bconv23\", shape=[50])\n",
    "\n",
    "#     Wconv31 = tf.get_variable(\"Wconv31\", shape=[3, 3, 50, 80])\n",
    "#     bconv31 = tf.get_variable(\"bconv31\", shape=[80])\n",
    "#     Wconv32 = tf.get_variable(\"Wconv32\", shape=[3, 3, 80, 80])\n",
    "#     bconv32 = tf.get_variable(\"bconv32\", shape=[80])\n",
    "#     Wconv33 = tf.get_variable(\"Wconv33\", shape=[3, 3, 80, 80])\n",
    "#     bconv33 = tf.get_variable(\"bconv33\", shape=[80])\n",
    "\n",
    "#     W1 = tf.get_variable(\"W1\", shape=[80, 512])\n",
    "#     b1 = tf.get_variable(\"b1\", shape=[512])\n",
    "#     W2 = tf.get_variable(\"W2\", shape=[512, 10])\n",
    "#     b2 = tf.get_variable(\"b2\", shape=[10])\n",
    "    \n",
    "#     # (con con con pool drop) *3 affine\n",
    "#     conv11 = tf.nn.conv2d(X, Wconv11, strides=[1,1,1,1], padding='SAME') + bconv11\n",
    "#     h11 = tf.nn.relu(conv11)\n",
    "#     conv12 = tf.nn.conv2d(h11, Wconv12, strides=[1,1,1,1], padding='SAME') + bconv12\n",
    "#     h12 = tf.nn.relu(conv12)\n",
    "#     conv13 = tf.nn.conv2d(h12, Wconv13, strides=[1,1,1,1], padding='SAME') + bconv13\n",
    "#     h13 = tf.nn.relu(conv13)\n",
    "#     h_pool1 = tf.nn.max_pool(h13, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')\n",
    "#     hidden1 = tf.nn.dropout(h_pool1, droprate)\n",
    "\n",
    "#     conv21 = tf.nn.conv2d(hidden1, Wconv21, strides=[1,1,1,1], padding='SAME') + bconv21\n",
    "#     h21 = tf.nn.relu(conv21)\n",
    "#     conv22 = tf.nn.conv2d(h21, Wconv22, strides=[1,1,1,1], padding='SAME') + bconv22\n",
    "#     h22 = tf.nn.relu(conv22)\n",
    "#     conv23 = tf.nn.conv2d(h22, Wconv23, strides=[1,1,1,1], padding='SAME') + bconv23\n",
    "#     h23 = tf.nn.relu(conv23)\n",
    "#     h_pool2 = tf.nn.max_pool(h23, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')\n",
    "#     hidden2 = tf.nn.dropout(h_pool2, droprate)\n",
    "    \n",
    "#     conv31 = tf.nn.conv2d(hidden2, Wconv31, strides=[1,1,1,1], padding='SAME') + bconv31\n",
    "#     h31 = tf.nn.relu(conv31)\n",
    "#     conv32 = tf.nn.conv2d(h31, Wconv32, strides=[1,1,1,1], padding='SAME') + bconv32\n",
    "#     h32 = tf.nn.relu(conv32)\n",
    "#     conv33 = tf.nn.conv2d(h32, Wconv33, strides=[1,1,1,1], padding='SAME') + bconv33\n",
    "#     h33 = tf.nn.relu(conv33)\n",
    "#     h_pool3 = tf.nn.max_pool(h33, ksize=[1, 8, 8, 1], strides=[1, 8, 8, 1], padding='SAME')\n",
    "#     hidden3 = tf.nn.dropout(h_pool3, droprate)\n",
    "    \n",
    "#     conv_flat = tf.reshape(hidden3,[-1,80])\n",
    "#     affine = tf.matmul(conv_flat,W1) + b1\n",
    "#     h4 = tf.nn.relu(affine)\n",
    "#     hidden4 = tf.nn.dropout(h4, droprate)\n",
    "#     y_out = tf.matmul(hidden4,W2) + b2\n",
    "#     return y_out\n",
    "\n",
    "# y_out = my_model2(X,y,is_training)\n",
    "# mean_loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=tf.one_hot(y,10), logits=y_out))\n",
    "\n",
    "# learning_rate = tf.train.exponential_decay(0.001, global_step, 100, 0.9, staircase=True)\n",
    "# optimizer = tf.train.AdamOptimizer(learning_rate).minimize(mean_loss, global_step=global_step)#, global_step=global_step\n",
    "\n",
    "# with tf.Session() as sess:\n",
    "#     with tf.device(\"/cpu:0\"): #\"/cpu:0\" or \"/gpu:0\" \n",
    "#         sess.run(tf.global_variables_initializer())\n",
    "#         print('Training')\n",
    "#         run_model(sess,y_out,mean_loss,X_train,y_train,100,64,100,optimizer)\n",
    "#         print('Validation')\n",
    "#         run_model(sess,y_out,mean_loss,X_val,y_val,1,64)\n",
    "#         print('Test')\n",
    "#         run_model(sess,y_out,mean_loss,X_test,y_test,1,64)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Going further with TensorFlow\n",
    "\n",
    "The next assignment will make heavy use of TensorFlow. You might also find it useful for your projects. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Extra Credit Description\n",
    "If you implement any additional features for extra credit, clearly describe them here with pointers to any code in this or other files if applicable."
   ]
  }
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